集合预报方法在大气环流模式延伸期降水预测中的应用研究
摘要
在当今全球气候变暖气候条件下，极端天气与气候事件频繁发生，除了1周以内的天气预报和月、季节时间尺度的短期气候预测以外，研究和开发6-30天延伸期数值预报新技术对于我们应对极端事件和持续性异常事件具有十分重要的意义。本文基于国家气候中心开发的大气环流模式（BCC_AGCM），主要针对6-30天延伸期降水预报方法开展了深入的研究。首先，我们利用1998年夏季的大量回报试验以及1996-2005年其他夏季的回报试验，构建了一种相对离散的时间滞后集合预报系统，采用ETS、HK、BIA和PC等降水评分标准，评估了等权重集合平均预报方法（LAF）在6-15天降水预报中的应用效果,结果表明该方法较确定性预报有明显改进；并提出了降水分类集合预报新方法（LCF），评估结果表明，该方法较LAF方法对我国夏季6-15天的降水预报能力又有一定程度的提高；最后针对月内逐侯时间间隔内逐日降水发生的频率进行了预报研究，结果表明LCF集合预报方法可以明显提高逐侯5天内降水发生概率的可预报性；此外，本文还探讨了不同模式分辨率对预报结果的影响。主要结论如下：
（1）通过检验不同滞后时长的LAF对全球500hPa位势高度的可预报性，发现对于BCC_AGCM延伸期预报而言，滞后3天的集合预报成员能够反映足够多的大尺度环流的有效初值信息。基于滞后3天不同时间间隔的LAF集合预报结果表明，使用24小时间隔的4个成员和使用12小时（或6小时）间隔的5个最新成员的LAF对我国夏季6-15天逐日1mm和5mm阈值降水改进明显，其中6小时间隔的LAF预报技巧相对最高（即最优LAF）；
（2）随着预报时间的增加，在传统概率集合预报显著离散的情况下，本文提出了一种时间滞后降水分类集合预报方法（LCF）。LCF方法本身并不是预报降水事件发生的概率，而是利用集合预报的概率信息预报某一强度分类降水是否发生，即在某一格点上，当预报该分类降水发生的集合预报概率超过某一阈值时，预报降水发生，否则不发生。基于滞后3天6小时间隔的13个集合成员，本文对不同概率阈值的LCF集合预报进行ETS、HK、BIA和PC评分，结果表明相对于确定性预报和最优LAF方法，5/13和4/13概率阈值的LCF方法分别可以最显著地改进6-15天1mm和5mm分类降水预报，尤其对1mm及以上降水（此后表示为1+mm）来说，相应的ETS和HK技巧评分整体上分别超过了0.1和0.2，准确率PC评分高于60%，而频率偏差BIA评分明显比最优LAF的评分值更接近1.0。此外，LCF对不同分类降水同样具有较好的预报效果，并且LCF的最优概率阈值随着分类降水强度的增加而减小；
（3）从预报准确率的空间分布来看，针对6-15天的降水预报，最优的LCF预报方法可以显著提高中国多雨区的降水预报，这些区域的1+mm/day实况降水发生频率≥40%-50%，主要集中在中国中部到南部，东北部以及青藏高原东南部，相应的PC评分值较确定性预报增加5%-15%。而最优LCF与最优LAF的PC评分之差的空间分布表明，最优LCF预报整体上优于最优LAF，相应的预报准确率提高约3%-6%，并且能够一定程度上减少LAF方法在中国干旱和半干旱地区可能存在的虚假预报；
（4）本文基于最优LCF方法针对月内逐侯降水发生频率进行了可预报分析，结果表明，最优LCF方法对逐侯5天内发生1天、2天和3天以上逐日1+mm降水的事件具有一定的预报参考价值，尤其是对于夏季实际1+mm/day降水发生频率大于50%的地区，相应的PC值分别能够达到70%-80%、60%-70%和50%-60%；
（5）通过对比分析BCC_AGCM T42粗分辨率和T106中等分辨率的不同回报试验结果，表明LAF和LCF方法在更高水平分辨率版下对6-15天1+mm和5+mm逐日降水预报同样具有可用性，其中LCF的最优概率阈值的选取对水平分辨率并不敏感。对于月内逐侯5天内逐日1+mm降水发生频率的预报，LCF方法仍然能够保持一定的可预报性。
In the background of global warming, extreme weather and climate eventsfrequently occur. Except weather forecasting in one week andsub-seasonal/seasonal climate prediction, researching and developing the newnumerical forecasting technique for6-30day extended-range prediction is alsovery significant to addressing these extreme events. Based on the Beijing ClimateCenter’s Atmospheric General Circulation Model, the main purpose of this paperis to study the forecast method of6-30day extended-range precipitation. First,we conducted a relatively spread time-lagged ensemble system by using a largenumber of hindcasts during1996-2005summers, and evaluated the equal weightensemble mean method (LAF) used to6-15day forecasts in terms of EquitableThreat Score (ETS), Frequency bias (BIA), Hanssen and Kuipers score (HK) andPropotion Correct (PC). It indicates that this method can improve thedeterministic forecast. Moreover, we suggested a precipitation categoricalensemble forecast method (LCF). It further shows a promise improvement for6-15day summer precipitation in China as compared to the ensemble mean. Lastbut not least, we studied predictability of the occurrence frequency of dailyprecipitation during every5days in30days, and the result indicates that the LCFcan significantly enhance this occurrence frequency prediction. In addition, wediscussed the influence of ensemble forecast from different resolutions in thispaper. The main conclutions as following:
(1) Through examining the LAF predictability of500hPa geopotential heightin the globe, we found that time-lagged forecasts within the last three days fromBCC_AGCM hindcasts could generally contain useful initial information in theextended-range. Based upon this result, the different time lag interval sensitiveexperiments show that the LAF using four members at24hours time lag intervalsand last five members at12(or6hours) time lag intervals can significantlyimprove the6-15day precipitation for1mm and5mm thresholds, comparatively,the LAF using6hours time interval members is optimal (i.e. the optimal LAF);
(2) As ensemble probabilistic forecasts get much more divergence withlonger lead time, we suggested a time-lagged categorical precipitation forecast(LCF) method. It is not a probability forecast but a categorical forecast ofprecipitation intensity at any model grid box. A given categorical precipitation isforecasted only when the ensemble probability for that categorical precipitationto occur at one grid box exceeds a certain threshold. Based upon13memberswith lagging3days at6hours intervals, the ETS, HK, BIA and PC scoreevaluations for the LCF with different probabilistic thresholds indicate that theLCF with5/13and4/13thresholds can most significantly improve the6-15day1mm and5mm categorical precipitation forecasts as compared with thedeterministic forecast and the optimal LAF, especially for the1mm rainfall andabove (1+mm), the correspondeing ETS and HK scores respectively increaseabove0.1and0.2, the PC score is higher than60%, and the frequency bias BIAscore is closer to1.0than the LAF. In addition, similar improvements by LCF arealso found for the prediction of several other categories of precipitation, and theoptimal threshold for LCF method to achieve the best results slightly increases asthe rainfall threshold decreases.
(3) The results of geographical distribution of6-15day forecasts accuracyrates further show that the improvements by LCF are primarily located over therainy regions where the frequencies of observed1+mm rainfall days during theforecast time period are larger than about40%-50%. These regions mainlyinclude central to southern, northeastern China and the northeastern part of theTibetan Plateau, the corresponding PC scores increase about5%-15%ascompared to the deterministic forecast. The geographical distribution ofdifferences of PC score between the optimal LCF and the optimal LAF indicatethat the LCF is generally better than the LAF, and the accuracy rates increaseabout3%-6%. Meanwhile, the LCF can decrease the LAF flase alarms in thepart of arid and semi-arid drought regions over China.
(4) In this work, the LCF method is also used to predict the occurrencefrequency of daily precipitation during every5days. The results show that thisfrequency larger than1day,2and3days during every5days is predictable byusing the LCF, especially the regions where the frequencies of1+mm/dayobserved rainfall days are larger than50%, the corresponding PC values are70%-80%,60%-70%and50%-60%, respectively.
(5) The comparation of ensembles respectively based on BCC_AGCM at T42resolution and T106resolution shows that LAF and LCF are also useful toimprove6-15day1+mm and5+mm daily precipitation forecasts at a highermodel resolution, and the optimal probabilistic threshold is not sensitive to themodel horizontal resolution. The occurrence frequency of1+mm dailyprecipitation during every5days is also predictable by using the optimal LCF.
（1）通过检验不同滞后时长的LAF对全球500hPa位势高度的可预报性，发现对于BCC_AGCM延伸期预报而言，滞后3天的集合预报成员能够反映足够多的大尺度环流的有效初值信息。基于滞后3天不同时间间隔的LAF集合预报结果表明，使用24小时间隔的4个成员和使用12小时（或6小时）间隔的5个最新成员的LAF对我国夏季6-15天逐日1mm和5mm阈值降水改进明显，其中6小时间隔的LAF预报技巧相对最高（即最优LAF）；
（2）随着预报时间的增加，在传统概率集合预报显著离散的情况下，本文提出了一种时间滞后降水分类集合预报方法（LCF）。LCF方法本身并不是预报降水事件发生的概率，而是利用集合预报的概率信息预报某一强度分类降水是否发生，即在某一格点上，当预报该分类降水发生的集合预报概率超过某一阈值时，预报降水发生，否则不发生。基于滞后3天6小时间隔的13个集合成员，本文对不同概率阈值的LCF集合预报进行ETS、HK、BIA和PC评分，结果表明相对于确定性预报和最优LAF方法，5/13和4/13概率阈值的LCF方法分别可以最显著地改进6-15天1mm和5mm分类降水预报，尤其对1mm及以上降水（此后表示为1+mm）来说，相应的ETS和HK技巧评分整体上分别超过了0.1和0.2，准确率PC评分高于60%，而频率偏差BIA评分明显比最优LAF的评分值更接近1.0。此外，LCF对不同分类降水同样具有较好的预报效果，并且LCF的最优概率阈值随着分类降水强度的增加而减小；
（3）从预报准确率的空间分布来看，针对6-15天的降水预报，最优的LCF预报方法可以显著提高中国多雨区的降水预报，这些区域的1+mm/day实况降水发生频率≥40%-50%，主要集中在中国中部到南部，东北部以及青藏高原东南部，相应的PC评分值较确定性预报增加5%-15%。而最优LCF与最优LAF的PC评分之差的空间分布表明，最优LCF预报整体上优于最优LAF，相应的预报准确率提高约3%-6%，并且能够一定程度上减少LAF方法在中国干旱和半干旱地区可能存在的虚假预报；
（4）本文基于最优LCF方法针对月内逐侯降水发生频率进行了可预报分析，结果表明，最优LCF方法对逐侯5天内发生1天、2天和3天以上逐日1+mm降水的事件具有一定的预报参考价值，尤其是对于夏季实际1+mm/day降水发生频率大于50%的地区，相应的PC值分别能够达到70%-80%、60%-70%和50%-60%；
（5）通过对比分析BCC_AGCM T42粗分辨率和T106中等分辨率的不同回报试验结果，表明LAF和LCF方法在更高水平分辨率版下对6-15天1+mm和5+mm逐日降水预报同样具有可用性，其中LCF的最优概率阈值的选取对水平分辨率并不敏感。对于月内逐侯5天内逐日1+mm降水发生频率的预报，LCF方法仍然能够保持一定的可预报性。
In the background of global warming, extreme weather and climate eventsfrequently occur. Except weather forecasting in one week andsub-seasonal/seasonal climate prediction, researching and developing the newnumerical forecasting technique for6-30day extended-range prediction is alsovery significant to addressing these extreme events. Based on the Beijing ClimateCenter’s Atmospheric General Circulation Model, the main purpose of this paperis to study the forecast method of6-30day extended-range precipitation. First,we conducted a relatively spread time-lagged ensemble system by using a largenumber of hindcasts during1996-2005summers, and evaluated the equal weightensemble mean method (LAF) used to6-15day forecasts in terms of EquitableThreat Score (ETS), Frequency bias (BIA), Hanssen and Kuipers score (HK) andPropotion Correct (PC). It indicates that this method can improve thedeterministic forecast. Moreover, we suggested a precipitation categoricalensemble forecast method (LCF). It further shows a promise improvement for6-15day summer precipitation in China as compared to the ensemble mean. Lastbut not least, we studied predictability of the occurrence frequency of dailyprecipitation during every5days in30days, and the result indicates that the LCFcan significantly enhance this occurrence frequency prediction. In addition, wediscussed the influence of ensemble forecast from different resolutions in thispaper. The main conclutions as following:
(1) Through examining the LAF predictability of500hPa geopotential heightin the globe, we found that time-lagged forecasts within the last three days fromBCC_AGCM hindcasts could generally contain useful initial information in theextended-range. Based upon this result, the different time lag interval sensitiveexperiments show that the LAF using four members at24hours time lag intervalsand last five members at12(or6hours) time lag intervals can significantlyimprove the6-15day precipitation for1mm and5mm thresholds, comparatively,the LAF using6hours time interval members is optimal (i.e. the optimal LAF);
(2) As ensemble probabilistic forecasts get much more divergence withlonger lead time, we suggested a time-lagged categorical precipitation forecast(LCF) method. It is not a probability forecast but a categorical forecast ofprecipitation intensity at any model grid box. A given categorical precipitation isforecasted only when the ensemble probability for that categorical precipitationto occur at one grid box exceeds a certain threshold. Based upon13memberswith lagging3days at6hours intervals, the ETS, HK, BIA and PC scoreevaluations for the LCF with different probabilistic thresholds indicate that theLCF with5/13and4/13thresholds can most significantly improve the6-15day1mm and5mm categorical precipitation forecasts as compared with thedeterministic forecast and the optimal LAF, especially for the1mm rainfall andabove (1+mm), the correspondeing ETS and HK scores respectively increaseabove0.1and0.2, the PC score is higher than60%, and the frequency bias BIAscore is closer to1.0than the LAF. In addition, similar improvements by LCF arealso found for the prediction of several other categories of precipitation, and theoptimal threshold for LCF method to achieve the best results slightly increases asthe rainfall threshold decreases.
(3) The results of geographical distribution of6-15day forecasts accuracyrates further show that the improvements by LCF are primarily located over therainy regions where the frequencies of observed1+mm rainfall days during theforecast time period are larger than about40%-50%. These regions mainlyinclude central to southern, northeastern China and the northeastern part of theTibetan Plateau, the corresponding PC scores increase about5%-15%ascompared to the deterministic forecast. The geographical distribution ofdifferences of PC score between the optimal LCF and the optimal LAF indicatethat the LCF is generally better than the LAF, and the accuracy rates increaseabout3%-6%. Meanwhile, the LCF can decrease the LAF flase alarms in thepart of arid and semi-arid drought regions over China.
(4) In this work, the LCF method is also used to predict the occurrencefrequency of daily precipitation during every5days. The results show that thisfrequency larger than1day,2and3days during every5days is predictable byusing the LCF, especially the regions where the frequencies of1+mm/dayobserved rainfall days are larger than50%, the corresponding PC values are70%-80%,60%-70%and50%-60%, respectively.
(5) The comparation of ensembles respectively based on BCC_AGCM at T42resolution and T106resolution shows that LAF and LCF are also useful toimprove6-15day1+mm and5+mm daily precipitation forecasts at a highermodel resolution, and the optimal probabilistic threshold is not sensitive to themodel horizontal resolution. The occurrence frequency of1+mm dailyprecipitation during every5days is also predictable by using the optimal LCF.
引文
陈静,薛纪善,颜宏(2005),一种新型的中尺度暴雨集合预报初值扰动方法研究,大气科学,29(5):717-726.
国家气候中心(1998),98中国大洪水与气候异常,北京；气象出版社,139pp.
颉卫华,吴统文(2010),全球大气环流模式对1998年夏季江淮流域强降水的回报试验研究,大气科学,34(5),962-978.
李崇银,顾薇(2010),2008年1月乌拉尔阻塞高压异常活动的分析研究,大气科学,34(5):865-874.
李维京,张培群,李清泉,王兰宁,刘益民,史学丽,张祖强,刘一鸣,胡国权,党鸿雁,张芳,陈丽娟,孙除荣,赵其庚,董敏(2005),动力气候模式预测系统业务化及其应用,应用气象学报,16(增刊):1-11.
颜宏(1987),初始方程P-σ混合坐标细网格嵌套模式的设计,高原气象，第6卷(增刊):64-139.
Baumhefner, D. P.(1996), Numerical Extended-Range Prediction: Forecast SkillUsing a Low-Resolution Climate Model, Mon. Wea. Rev.,124,1965-1980.Chou, J. F.(1983), The decay of the influences of initial fields andsome properties of their operations, Acta Meteor. Sinica,41:385-392.
Béland, M., J. Mitchell, L. Barrie et al.(2009), Priorities in Strengthening andPromoting Linkages between Climate, Weather, Water and EnvironmentalPrediction Research and Services, Pre-CAS Public Web Consultation, WMOCAS XV, Seoul, Korea.
Brier, G. W.(1950), Verification of forecasts expressed in term of probability,Mon. Wea. Rev.78,1-3.
Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli (1999), ProbabilisticPredictions of Precipitation Using the ECMWF Ensemble Prediction System,Wea. Forecasting,14,168-189.
Bukovsky, M. S., D. J. Karoly (2009), A Note on Precipitation Simulations usingWRF as a Nested Regional Climate Model, Journal of Applied Meteorologyand Climatology: In Press
Chen, H., R. Yu, J. Li, X. Xin, Z. Wang, and T. Wu (2011), The coherentinterdecadal changes of East Asia climate in mid-summer simulated byBCC_AGCM2.0.1, Climate Dynamics, doi:10.1007/s00382-011-1154-6.
Chessa, P. A., and F. Lalaurette (2001), Verification of the ECMWF EnsemblePrediction System Forecasts: A Study of Large-Scale Patterns, Wea.Forecasting.,16,611-619.
Chou, J. F.(1989), Predictability of the Atmosphere, Adv Atmos Sci,6:335-346.
Cocke, S., H. S. Lee, G. H. Lim et al.(2006), The NCAR CCM as a NumericalWeather Prediction Model, Mon. Wea. Rev.,134:1954-1971.
Cressman, G. P.(1959), An operational objective analysis system, Mon. Wea. Rev.87,367-374.
Dalcher, A., E. Kalnay, and R. N. Hoffman (1988), Medium range laggedforecasts, Mon. Wea. Rev.,116,402-416.
Ding, Y. and Hu, G.(2003), A study on water vapor budget over China during the1998severe flood periods, Acta Meteorologica Sinica (in Chinese).61(2),129-145.
Dong, M., T. Wu，Z. Wang et al.(2009), Simulations of the tropical intra-seasonaloscillations by the AGCM of the Beijing Climate Center, Acta Meteorol Sin,67(6),912-922
Du, J., S. L. Mullen and F. Sanders (1997), Short-range ensemble forecasting ofquantitative precipitation, Mon. Wea. Rev.125,2427-2459.
Ebert, E. E.(2001), Ability of a Poor Man's Ensemble to Predict the Probabilityand Distribution of Precipitation, Mon. Wea. Rev.,129,2461-2480.
Eckel, F. A., and M. K. Walters (1998), Calibrated Probabilistic QuantitativePrecipitation Forecasts Based on the MRF Ensemble, Wea. Forecasting,13,1132-1147.
Finley, J. P.,1884: Tornado predictions. Amer. Meteor. J.,1,85-88.
Fritsch, J. M., J. Hilliker, J. Ross and R. L. Vislocky (2000), Model consensus,Wea. Forecasting,15,571-582.
Gneiting, T. and A. E. Raftery (2005), Weather forecasting with ensemblemethods, Science (New York, N.Y.).310(5746),248-9.
Hamill, T. M., and S. J. Colucci (1997), Verification of eta-RSM short-rangeensemble forecasts, Mon. Wea. Rev.,125,1312-1327.
Hamill, T. M. and S. J. Colucci (1998), Evaluation of Eta–RSM ensembleprobabilistic precipitation forecasts, Mon. Wea. Rev.126,711-724.
Hamill, T. M.(2001), Interpretation of rank histograms for verifying ensembleforecasts. Mon, Wea. Rev.129,550-560.
Hamill, T. M., J. S. Whitaker, and X. Wei (2004), Ensemble reforecasting:Improving medium-range forecast skill using retrospective forecasts, Mon.Wea. Rev.132,1434-1447.
Hamill, T. M. and J. Juras (2006), Measuring forecast skill: is it real skill or is itthe varying climatology?, Q.J.R. Meteorol. Soc.,132:2905-2923.
Hamill, T. M., R. Hagedorn, and J. S. Whitaker (2008), Probabilistic ForecastCalibration Using ECMWF and GFS Ensemble Reforecasts. Part II:Precipitation, Mon. Wea. Rev.,136,2620-2632.
Hannay, C., D. L. Williamson, J. J. Hack, J. T. Kiehl, J. G. Olson, S. A. Klein, C.S. Bretherton, M. K hler (2009), Evaluation of Forecasted Southeast PacificStratocumulus in the NCAR, GFDL, and ECMWF Models, J. Climate,22,2871-2889.
Hanssen, A. W., W. J. A. Kuipers (1965), On the relationship between thefrequency of rain and various meteorological parameters, Meded. Verh.,81,2-15.
Hoffman, R. N., E. Kalnay (1983), Lagged average forecasting, an alternative toMonte Carlo forecasting, Tellus,35A:100-118.
Hoffman, R. N.(2002), Controlling The Global Weather, Bull. Amer. Meteor.Soc.,83:241-248.
Houtekamer, P. L., and H. L. Mitchell (1998), Data assimilation using anensemble Kalman filter technique, Mon. Wea. Rev.,126,196-811.
Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki(2009), A Unified Modeling Approach to Climate System Prediction, Bull.Amer. Meteor. Soc.,90,(12):1820-1832.
Jan van Oldenborgh G., M. A. Balmaseda, L. Ferranti et al.(2005), Evaluation ofAtmospheric Fields from the ECMWF Seasonal Forecasts over a15-YearPeriod, J. Climate,18:3250-3269.
John, L. M. and E. E. Ebert (1999), Verification of Quantitative PrecipitationForecasts from Operational Numerical Weather Prediction Models overAustralia, Weather and Forecasting,15,103-121.
Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, and E.Williford (2000), Multimodel ensemble forecasts for weather and seasonalclimate, J. Clim.,13,4196-4216.
Lau, N. C., and J. J. Ploshay (2009), Simulation of Synoptic-andSubsynoptic-Scale Phenomena Associated with the East Asian SummerMonsoon Using a High-Resolution GCM, Mon. Wea. Rev.,137,137-160.
Leith, C. E.(1974), Theoretical skill of Monte Carlo forecasts, Mon. Wea. Rev.,102,409-418.
Liu, Y.(2003), Prediction of monthly-seasonal precipitation using coupled SVDpatterns between soil moisture and subsequent precipitation, Geophys. Res.Lett.,30,1827, doi:10.1029/2003GL017709.
Lorenz E. N.(1963), Deterministic Nonperiodic Flow, J. Atmos. Sci.20,130-141.
Lorenz E. N.(1982), Atmospheric predictability experiments with a largenumerical model, Tellus,34:505-513.
Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin (2007), Short-rangenumerical weather prediction using time-lagged ensembles, Wea. Forecasting,22,580-595.
McLay, G. J.(2008), Markov Chain Modeling of Sequences of Lagged NWPEnsemble Probability Forecasts: An Exploration of Model Properties andDecision Support Applications, Mon. Wea. Rev.,136,3655-3670.
Molteni, F., R. Buizza, T. N. Palmer et al.(1996), The ECMWF ensembleprediction system: methodology and validation, Quart. J. Roy. Meteor. Soc.,122,73-119.
Moore, A. M., and R. Kleeman (1998), Skill assessment for ENSO usingensemble prediction, Quart. J. Roy. Meteor. Soc.,124,557-584.
Mu, M., W. S. Duan, B. Wang (2003), Conditional nonlinear optimal perturbationand its application, Nonlinear Process Geophys,10:493-501.
Mullen, S. L. and R. Buizza (2001), Quantitative precipitation forecasts over theUnited States by the ECMWF Ensemble Prediction System, Mon. Wea. Rev.129,638-663.
Pan, J., H. van den Dool (1998), Extended-Range Probability Forecasts Based onDynamical Model Output, Wea. Forecasting,13,983-996.
Peel, S., and L. J. Wilson (2008), A Diagnostic Verification of the PrecipitationForecasts Produced by the Canadian Ensemble Prediction System, Wea.Forecasting,23,596-616.
Qin, J., and H. M. van den Dool (1996), Simple extensions of an NWP model,Mon. Wea. Rev.,124,277-287.
Romatschke, U., R. A. Houze (2011), Characteristics of Precipitating ConvectiveSystems in the South Asian Monsoon, J. Hydrometeor.12,3-26.
Saha, S., and H. M. van den Dool (1988), A measure of the practical limit ofpredictability, Mon. Wea. Rev.,116,2522-2526.
Schaefer, J. T.(1990), The critical success index as an indicator of warning skill,Wea. Forecasting,5,570-575.
Schmeits, J. M. and K. J. Kok (2010), A Comparison between Raw EnsembleOutput,(Modified) Bayesian Model Averaging, and Extended LogisticRegression Using ECMWF Ensemble Precipitation Reforecasts, Mon. Wea.Rev.,138,4199-4211.
Sivillo, J. K., J. E. Ahlquist and Z. Toth (1997), An Ensemble Forecasting Primer,Wea. Forecasting,12,809-818.
Stensrud, D. J., and N. Yussouf (2007), Reliable Probabilistic QuantitativePrecipitation Forecasts from a Short-Range Ensemble Forecasting System,Wea. Forecasting,22,3-17.
Tippett, M. K., A. G. Barnston, A. W. Robertson (2007), Estimation of SeasonalPrecipitation Tercile-Based Categorical Probabilities from Ensembles, J.Climate,20,2210-2228.
Toth, Z., Y. Kalany (1993), Ensemble forecasting at NMC: The generation ofperturbations, Bull A mer. Meteor Soc.,74,2317-2330.
Toth, Z., Y. Kalany (1997), Ensemble forecasting at NCEP: the breeding method,Mon. Wea. Rev.,125,3297-3318.
Tracton, M. S., K. Mo, W. Chen, E. Kalnay, R. Kistler and G. White (1989),Dynamical Extended Range Forecasting (DERF) at the NationalMeteorological Center, Mon. Wea. Rev.,117,1604-1635.
Van den Dool, H., and L. Rukhovets (1994), On the weights for anensemble-averaged6-10day forecast, Wea. Forecasting,9,457-65.
Vitart, F., and F. Molteni (2009), Dynamical Extended-Range Prediction of EarlyMonsoon Rainfall over India, Mon. Wea. Rev.,137,1480-1492.
Walser, A., D. Luthi, and C. Schar (2004), Predictability of precipitation in acloud-resolving model, Mon. Wea. Rev.,132,560-577.
Whitaker, J. S., X. Wei, and F. Vitart (2006), Improving Week-2Forecasts withMultimodel Reforecast Ensembles, Mon. Wea. Rev.134,2279-2284.
Wilks, D.S.(1995), Statistical Methods in the Atmospheric Sciences, AcademicPress,467pp.
WMO (1999), Commission for basic systems abridged final report withresolutions and recommendations, Rep. WMO893, Geneva, Switzerland,184pp.
Wu T., and G. Wu (2004), An empirical formula to compute snow cover fractionin GCMs, Adv Atmos Sci,21:529-535. doi:10.1007/BF02915720.
Wu, T., R. Yu, F. Zhang et al.(2008), A modified dynamic framework foratmospheric spectral model and its application, J. Atmos.Sci.,65,2235-2253.
Wu, T., R. Yu, F. Zhang et al.(2010), The Beijing Climate Center forAtmospheric General Circulation Model (BCC-AGCM2.0.1): Description andits performance for the present-day climate, Clim. Dyn.,34,123-147.
Wu, T.(2012), A mass-flux cumulus parameterization scheme forlarge-scalemodels: description and test with observations, Clim Dyn,38:725–744.
Wu, T., W. Li, J. Ji et al.(2013), Global carbon budgets simulated by the BeijingClimate Center Climate System Model for the last century, J. Geophys. Res., inpress.
Yuan, H., C. Lu, J. A. McGinley, P. J. Schultz, B. D. Jamison, L. Wharton, and C.J. Anderson (2009), Evaluation of short-range quantitative precipitationforecasts from a time-lagged multimodel ensemble, Wea. Forecasting,24,18-38.
Zhang G. J., M. Mu (2005), Effects of modifications to the Zhang-McFarlaneconvection parameterization on the simulation of thetropical precipitation inthe National Center for AtmosphericResearch Community Climate Model,version3, J. Geophys. Res.,110: D09109.
Zhu, Y., Z. Toth, R. Wobus, M. Wei and B. Cui (cited2012), May2006upgradeof the GEFS and first implementation of NAEFS systems.[Avaliable online athttp://www.emc.ncep.noaa.gov/gmb/ens/ens_imp_news.html.]
国家气候中心(1998),98中国大洪水与气候异常,北京；气象出版社,139pp.
颉卫华,吴统文(2010),全球大气环流模式对1998年夏季江淮流域强降水的回报试验研究,大气科学,34(5),962-978.
李崇银,顾薇(2010),2008年1月乌拉尔阻塞高压异常活动的分析研究,大气科学,34(5):865-874.
李维京,张培群,李清泉,王兰宁,刘益民,史学丽,张祖强,刘一鸣,胡国权,党鸿雁,张芳,陈丽娟,孙除荣,赵其庚,董敏(2005),动力气候模式预测系统业务化及其应用,应用气象学报,16(增刊):1-11.
颜宏(1987),初始方程P-σ混合坐标细网格嵌套模式的设计,高原气象，第6卷(增刊):64-139.
Baumhefner, D. P.(1996), Numerical Extended-Range Prediction: Forecast SkillUsing a Low-Resolution Climate Model, Mon. Wea. Rev.,124,1965-1980.Chou, J. F.(1983), The decay of the influences of initial fields andsome properties of their operations, Acta Meteor. Sinica,41:385-392.
Béland, M., J. Mitchell, L. Barrie et al.(2009), Priorities in Strengthening andPromoting Linkages between Climate, Weather, Water and EnvironmentalPrediction Research and Services, Pre-CAS Public Web Consultation, WMOCAS XV, Seoul, Korea.
Brier, G. W.(1950), Verification of forecasts expressed in term of probability,Mon. Wea. Rev.78,1-3.
Buizza, R., A. Hollingsworth, F. Lalaurette, and A. Ghelli (1999), ProbabilisticPredictions of Precipitation Using the ECMWF Ensemble Prediction System,Wea. Forecasting,14,168-189.
Bukovsky, M. S., D. J. Karoly (2009), A Note on Precipitation Simulations usingWRF as a Nested Regional Climate Model, Journal of Applied Meteorologyand Climatology: In Press
Chen, H., R. Yu, J. Li, X. Xin, Z. Wang, and T. Wu (2011), The coherentinterdecadal changes of East Asia climate in mid-summer simulated byBCC_AGCM2.0.1, Climate Dynamics, doi:10.1007/s00382-011-1154-6.
Chessa, P. A., and F. Lalaurette (2001), Verification of the ECMWF EnsemblePrediction System Forecasts: A Study of Large-Scale Patterns, Wea.Forecasting.,16,611-619.
Chou, J. F.(1989), Predictability of the Atmosphere, Adv Atmos Sci,6:335-346.
Cocke, S., H. S. Lee, G. H. Lim et al.(2006), The NCAR CCM as a NumericalWeather Prediction Model, Mon. Wea. Rev.,134:1954-1971.
Cressman, G. P.(1959), An operational objective analysis system, Mon. Wea. Rev.87,367-374.
Dalcher, A., E. Kalnay, and R. N. Hoffman (1988), Medium range laggedforecasts, Mon. Wea. Rev.,116,402-416.
Ding, Y. and Hu, G.(2003), A study on water vapor budget over China during the1998severe flood periods, Acta Meteorologica Sinica (in Chinese).61(2),129-145.
Dong, M., T. Wu，Z. Wang et al.(2009), Simulations of the tropical intra-seasonaloscillations by the AGCM of the Beijing Climate Center, Acta Meteorol Sin,67(6),912-922
Du, J., S. L. Mullen and F. Sanders (1997), Short-range ensemble forecasting ofquantitative precipitation, Mon. Wea. Rev.125,2427-2459.
Ebert, E. E.(2001), Ability of a Poor Man's Ensemble to Predict the Probabilityand Distribution of Precipitation, Mon. Wea. Rev.,129,2461-2480.
Eckel, F. A., and M. K. Walters (1998), Calibrated Probabilistic QuantitativePrecipitation Forecasts Based on the MRF Ensemble, Wea. Forecasting,13,1132-1147.
Finley, J. P.,1884: Tornado predictions. Amer. Meteor. J.,1,85-88.
Fritsch, J. M., J. Hilliker, J. Ross and R. L. Vislocky (2000), Model consensus,Wea. Forecasting,15,571-582.
Gneiting, T. and A. E. Raftery (2005), Weather forecasting with ensemblemethods, Science (New York, N.Y.).310(5746),248-9.
Hamill, T. M., and S. J. Colucci (1997), Verification of eta-RSM short-rangeensemble forecasts, Mon. Wea. Rev.,125,1312-1327.
Hamill, T. M. and S. J. Colucci (1998), Evaluation of Eta–RSM ensembleprobabilistic precipitation forecasts, Mon. Wea. Rev.126,711-724.
Hamill, T. M.(2001), Interpretation of rank histograms for verifying ensembleforecasts. Mon, Wea. Rev.129,550-560.
Hamill, T. M., J. S. Whitaker, and X. Wei (2004), Ensemble reforecasting:Improving medium-range forecast skill using retrospective forecasts, Mon.Wea. Rev.132,1434-1447.
Hamill, T. M. and J. Juras (2006), Measuring forecast skill: is it real skill or is itthe varying climatology?, Q.J.R. Meteorol. Soc.,132:2905-2923.
Hamill, T. M., R. Hagedorn, and J. S. Whitaker (2008), Probabilistic ForecastCalibration Using ECMWF and GFS Ensemble Reforecasts. Part II:Precipitation, Mon. Wea. Rev.,136,2620-2632.
Hannay, C., D. L. Williamson, J. J. Hack, J. T. Kiehl, J. G. Olson, S. A. Klein, C.S. Bretherton, M. K hler (2009), Evaluation of Forecasted Southeast PacificStratocumulus in the NCAR, GFDL, and ECMWF Models, J. Climate,22,2871-2889.
Hanssen, A. W., W. J. A. Kuipers (1965), On the relationship between thefrequency of rain and various meteorological parameters, Meded. Verh.,81,2-15.
Hoffman, R. N., E. Kalnay (1983), Lagged average forecasting, an alternative toMonte Carlo forecasting, Tellus,35A:100-118.
Hoffman, R. N.(2002), Controlling The Global Weather, Bull. Amer. Meteor.Soc.,83:241-248.
Houtekamer, P. L., and H. L. Mitchell (1998), Data assimilation using anensemble Kalman filter technique, Mon. Wea. Rev.,126,196-811.
Hurrell, J., G. A. Meehl, D. Bader, T. L. Delworth, B. Kirtman, and B. Wielicki(2009), A Unified Modeling Approach to Climate System Prediction, Bull.Amer. Meteor. Soc.,90,(12):1820-1832.
Jan van Oldenborgh G., M. A. Balmaseda, L. Ferranti et al.(2005), Evaluation ofAtmospheric Fields from the ECMWF Seasonal Forecasts over a15-YearPeriod, J. Climate,18:3250-3269.
John, L. M. and E. E. Ebert (1999), Verification of Quantitative PrecipitationForecasts from Operational Numerical Weather Prediction Models overAustralia, Weather and Forecasting,15,103-121.
Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, and E.Williford (2000), Multimodel ensemble forecasts for weather and seasonalclimate, J. Clim.,13,4196-4216.
Lau, N. C., and J. J. Ploshay (2009), Simulation of Synoptic-andSubsynoptic-Scale Phenomena Associated with the East Asian SummerMonsoon Using a High-Resolution GCM, Mon. Wea. Rev.,137,137-160.
Leith, C. E.(1974), Theoretical skill of Monte Carlo forecasts, Mon. Wea. Rev.,102,409-418.
Liu, Y.(2003), Prediction of monthly-seasonal precipitation using coupled SVDpatterns between soil moisture and subsequent precipitation, Geophys. Res.Lett.,30,1827, doi:10.1029/2003GL017709.
Lorenz E. N.(1963), Deterministic Nonperiodic Flow, J. Atmos. Sci.20,130-141.
Lorenz E. N.(1982), Atmospheric predictability experiments with a largenumerical model, Tellus,34:505-513.
Lu, C., H. Yuan, B. E. Schwartz, and S. G. Benjamin (2007), Short-rangenumerical weather prediction using time-lagged ensembles, Wea. Forecasting,22,580-595.
McLay, G. J.(2008), Markov Chain Modeling of Sequences of Lagged NWPEnsemble Probability Forecasts: An Exploration of Model Properties andDecision Support Applications, Mon. Wea. Rev.,136,3655-3670.
Molteni, F., R. Buizza, T. N. Palmer et al.(1996), The ECMWF ensembleprediction system: methodology and validation, Quart. J. Roy. Meteor. Soc.,122,73-119.
Moore, A. M., and R. Kleeman (1998), Skill assessment for ENSO usingensemble prediction, Quart. J. Roy. Meteor. Soc.,124,557-584.
Mu, M., W. S. Duan, B. Wang (2003), Conditional nonlinear optimal perturbationand its application, Nonlinear Process Geophys,10:493-501.
Mullen, S. L. and R. Buizza (2001), Quantitative precipitation forecasts over theUnited States by the ECMWF Ensemble Prediction System, Mon. Wea. Rev.129,638-663.
Pan, J., H. van den Dool (1998), Extended-Range Probability Forecasts Based onDynamical Model Output, Wea. Forecasting,13,983-996.
Peel, S., and L. J. Wilson (2008), A Diagnostic Verification of the PrecipitationForecasts Produced by the Canadian Ensemble Prediction System, Wea.Forecasting,23,596-616.
Qin, J., and H. M. van den Dool (1996), Simple extensions of an NWP model,Mon. Wea. Rev.,124,277-287.
Romatschke, U., R. A. Houze (2011), Characteristics of Precipitating ConvectiveSystems in the South Asian Monsoon, J. Hydrometeor.12,3-26.
Saha, S., and H. M. van den Dool (1988), A measure of the practical limit ofpredictability, Mon. Wea. Rev.,116,2522-2526.
Schaefer, J. T.(1990), The critical success index as an indicator of warning skill,Wea. Forecasting,5,570-575.
Schmeits, J. M. and K. J. Kok (2010), A Comparison between Raw EnsembleOutput,(Modified) Bayesian Model Averaging, and Extended LogisticRegression Using ECMWF Ensemble Precipitation Reforecasts, Mon. Wea.Rev.,138,4199-4211.
Sivillo, J. K., J. E. Ahlquist and Z. Toth (1997), An Ensemble Forecasting Primer,Wea. Forecasting,12,809-818.
Stensrud, D. J., and N. Yussouf (2007), Reliable Probabilistic QuantitativePrecipitation Forecasts from a Short-Range Ensemble Forecasting System,Wea. Forecasting,22,3-17.
Tippett, M. K., A. G. Barnston, A. W. Robertson (2007), Estimation of SeasonalPrecipitation Tercile-Based Categorical Probabilities from Ensembles, J.Climate,20,2210-2228.
Toth, Z., Y. Kalany (1993), Ensemble forecasting at NMC: The generation ofperturbations, Bull A mer. Meteor Soc.,74,2317-2330.
Toth, Z., Y. Kalany (1997), Ensemble forecasting at NCEP: the breeding method,Mon. Wea. Rev.,125,3297-3318.
Tracton, M. S., K. Mo, W. Chen, E. Kalnay, R. Kistler and G. White (1989),Dynamical Extended Range Forecasting (DERF) at the NationalMeteorological Center, Mon. Wea. Rev.,117,1604-1635.
Van den Dool, H., and L. Rukhovets (1994), On the weights for anensemble-averaged6-10day forecast, Wea. Forecasting,9,457-65.
Vitart, F., and F. Molteni (2009), Dynamical Extended-Range Prediction of EarlyMonsoon Rainfall over India, Mon. Wea. Rev.,137,1480-1492.
Walser, A., D. Luthi, and C. Schar (2004), Predictability of precipitation in acloud-resolving model, Mon. Wea. Rev.,132,560-577.
Whitaker, J. S., X. Wei, and F. Vitart (2006), Improving Week-2Forecasts withMultimodel Reforecast Ensembles, Mon. Wea. Rev.134,2279-2284.
Wilks, D.S.(1995), Statistical Methods in the Atmospheric Sciences, AcademicPress,467pp.
WMO (1999), Commission for basic systems abridged final report withresolutions and recommendations, Rep. WMO893, Geneva, Switzerland,184pp.
Wu T., and G. Wu (2004), An empirical formula to compute snow cover fractionin GCMs, Adv Atmos Sci,21:529-535. doi:10.1007/BF02915720.
Wu, T., R. Yu, F. Zhang et al.(2008), A modified dynamic framework foratmospheric spectral model and its application, J. Atmos.Sci.,65,2235-2253.
Wu, T., R. Yu, F. Zhang et al.(2010), The Beijing Climate Center forAtmospheric General Circulation Model (BCC-AGCM2.0.1): Description andits performance for the present-day climate, Clim. Dyn.,34,123-147.
Wu, T.(2012), A mass-flux cumulus parameterization scheme forlarge-scalemodels: description and test with observations, Clim Dyn,38:725–744.
Wu, T., W. Li, J. Ji et al.(2013), Global carbon budgets simulated by the BeijingClimate Center Climate System Model for the last century, J. Geophys. Res., inpress.
Yuan, H., C. Lu, J. A. McGinley, P. J. Schultz, B. D. Jamison, L. Wharton, and C.J. Anderson (2009), Evaluation of short-range quantitative precipitationforecasts from a time-lagged multimodel ensemble, Wea. Forecasting,24,18-38.
Zhang G. J., M. Mu (2005), Effects of modifications to the Zhang-McFarlaneconvection parameterization on the simulation of thetropical precipitation inthe National Center for AtmosphericResearch Community Climate Model,version3, J. Geophys. Res.,110: D09109.
Zhu, Y., Z. Toth, R. Wobus, M. Wei and B. Cui (cited2012), May2006upgradeof the GEFS and first implementation of NAEFS systems.[Avaliable online athttp://www.emc.ncep.noaa.gov/gmb/ens/ens_imp_news.html.]